There are biases in climate model simulations compared to the observations, which makes it hard to directly use model simulations to drive the impact models. In the present study, the authors try to correct biases in daily precipitation simulated by a regional climate model (RegCM4.4) based on probability distribution (Quantile-Mapping) over China. Transfer functions are established from the reference period 1991-2000, and then applied to the period 2001-2010 to validate the performance of the method. Six different methods using parametric or nonparametric transformations are employed and compared to observations. Results show that all the six methods can effectively reduce the biases of the precipitation simulated, the RQUANT (Non-parametric quantile mapping using robust empirical quantiles) is found to perform better than other methods. Further analysis shows that RQUANT can significantly improve the simulation of the mean precipitation and the interannual variability and extreme events.